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首页> 外文期刊>Journal of vector borne diseases >Climatic variables and malaria incidence in Dehradun, Uttaranchal, India.
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Climatic variables and malaria incidence in Dehradun, Uttaranchal, India.

机译:印度北方州博拉南德的气候变量和疟疾发病率。

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BACKGROUND & OBJECTIVES: Mosquito-borne diseases particularly malaria and Japanese encephalitis (JE) are becoming most dreaded health problems in Dehradun district. Keeping in view that the climatic factors particularly temperature and rainfall may alter the distribution of vector species--increasing or decreasing the ranges, depending on weather conditions that are favourable or unfavourable for mosquito breeding, it is aimed to find out the effect of climatic factors on malaria incidence with particular emphasis to capture the essential events as a result of climatic variability. METHODS: Mosquito sampling and identification was done using WHO entomological methods and follow-up of recognised keys and catalogues. Data on malaria incidence and meteorological information were gathered in a collaborative study with the District Malaria Office, and the Forest Research Institute, Dehradun respectively. Pearson's correlation analysis was applied for establishing relationship between climate variables and malaria transmission. RESULTS: Higher positive correlation of association was found between monthly parasite incidence and climatic variables (temperature, rainfall and humidity). However, highest significant correlation was found between rainfall and malaria incidence (r = 0.718, p < 0.0001) when the data were staggered to allow a lag of one-month. INTERPRETATION & CONCLUSION: Climatic variables that predict the presence or absence of malaria are likely to be the best suited for forecasting the distribution of this disease at the edges of its range.
机译:背景与目标:蚊子育疾病特别是疟疾和日本脑炎(JE)在Dehradun区成为最可怕的健康问题。鉴于认为气候因素特别温度和降雨可能会改变载体物种的分布 - 增加或减少范围,这取决于蚊虫育种的天气条件,旨在找出气候因素的影响疟疾发病率特别强调导致气候变异性导致基本事件。方法:采用蚊子采样和识别,使用WHO昆虫学方法和识别的钥匙和目录的后续完成。疟疾发病率和气象信息的数据分别在与区疟疾办事处和森林研究所,Dehradun的合作研究中聚集在一起。 Pearson的相关性分析应用于建立气候变量与疟疾传播之间的关系。结果:每月寄生虫发生率和气候变量(温度,降雨和湿度)之间发现了较高的阳性相关性。然而,当数据交错时,降雨和疟疾发病率之间发现了最高的显着相关性(R = 0.718,P <0.0001),以允许一个月的滞后。解释与结论:预测疟疾存在或不存在的气候变量可能是最适合预测其范围边缘的这种疾病的分布。

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